Inductive loop sensors are currently the most invested and widely deployed traffic detection infrastructure for traffic data collection from highways and traffic signal control in the United States. Inductive Loop Detection (ILD) technology is very reliable and provides accurate traffic count data. Properly installed loop sensors have a service life that is typically comparable to the road surface itself.
When installed in a double loop speed trap configuration, an inductive loop can provide point speed and effective vehicle length information. The double loop sensors are widely applied in Continuous Vehicle Classification (CVC) and Weigh-In-Motion (WIM) sites. However, most of the traffic detection stations are Continuous Count Stations (CCS), which are commonly installed in a single loop configuration that can provide traffic count and occupancy data only.
Therefore, when installed in a single loop configuration, aggregate traffic speed is usually estimated using other methods (Coifman and Kim, 2009; Wang and Nihan, 2003; Wang and Nihan, 2000; Lin et al., 2004) which assume some representative static or dynamic effective vehicle length value commonly known as the g-factor. However, such approach does not perform well to yield reliable estimates under congestion or under heavy truck traffic (Coifman and Neelisetty, 2014).
Unlike the conventional ILD systems that provide bivalent outputs to indicate vehicle presence, the Inductive Loop Signature-based detector systems measure and output the inductance changes of the inductive loops. The series of inductance changes caused by a traversing vehicle produce a waveform output and is referred as inductive loop signature or inductive vehicle signature (or simply “vehicle signature”). With the availability of inductive loop signature technology, researchers have demonstrated the advantages of using individual vehicle signature to generate vehicle speeds using single loop sensors (Tok et al., 2009; Park et al., 2010; Oh et al., 2002; Sun and Ritchie, 1999). These approaches can be summarized in two steps in general: (1) assigning vehicles to predetermined groups with homogeneous attributes via clustering analysis and/or neural network training; and (2) applying estimation models assigned to each group to generate speed estimates.
Vehicle classification has been implemented by the Federal Highway Administration (FHWA) to assist in evaluating highway use and developing policies, standards, and procedures. FHWA defines 13 vehicle classes which are defined by factors such as the number of wheels, axles, and trailers.